Simulating ion transport in electrolyte materials with physics-based and machine-learning models

Abstract: Electrolytes are indispensable components of electrochemical devices such as batteries, fuel cells, and supercapacitors, and the mass transport in electrolytes is one of the most important design focuses of such devices. A microscopic picture of ion transport is essential to link the chemical properties of electrolyte materials to their electrochemical applications. This thesis aims to establish such a connection through computer simulations of the transport phenomena, using a combination of physics-based and machine-learning methods.The first part of the thesis concerns the study of transport phenomena with molecular dynamics simulations, where the atomistic interactions are described by physics-based classical force fields. Guided by the principles of non-equilibrium statistical mechanics, the simulations reveal governing factors of ion transport in different systems. This is exemplified by the leading contribution of hydrodynamic interactions in the non-ideal ionic conductivity, and the qualitative distinction between transient and long-lived ion pairs. This approach also aids the interpretation and comparison of experiments and simulations, by elucidating their intrinsic constraints imposed by the reference frame, and their proper inter-conversions.The second part of the thesis aims to remedy a major limitation of the physics-based approach, namely the difficulty of accurately simulating complex reactive systems. The machine learning methods were developed to systematically generate the models from electronic structure calculations. The strength of this approach is demonstrated by showing how it correctly predicted the transport coefficients of proton-conducting materials with the desired accuracy. Limitations of this data-driven approach are also investigated, demonstrating the potential pitfall in the parameterization process, and leading to the development of an adaptive learn-on-the-fly workflow.Overall, the present thesis showcases how computer simulations can lead to insights regarding the ion transport in electrolyte materials, and how the development of machine-learning methods could empower those simulations to tackle complex and reactive systems.